Screen LibraryNature-inspired optimization algorithms /
Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-cho...
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| Main Authors: | |
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| Published: |
Elsevier,
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| Publisher Address: | Amsterdam ; Waltham, MA : |
| Publication Dates: | 2014. |
| Literature type: | Book |
| Language: | English |
| Edition: | First edition. |
| Subjects: | |
| Summary: |
Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning |
| Carrier Form: | xii, 263 pages : illustrations ; 24 cm |
| Bibliography: | Includes bibliographical references. |
| ISBN: |
9780124167438 : 0124167438 |
| Index Number: | QA402 |
| CLC: | O224 |
| Call Number: | O224/Y229 |
| Contents: | 1. Introduction to algorithms -- 2. Analysis of algorithms -- 3. Random walks and optimization -- 4. Simulated annealing -- 5. Genetic algorithms -- 6. Differential evolution -- 7. Particle swarm optimization -- 8. Firefly algorithms -- 9. Cuckoo search -- 10. Bat algorithms -- Flower pollination algorithms -- 12. A framework for self-tuning algorithms -- 13. How to deal with constraints -- 14. Multi-objective optimization -- 15. Other algorithms and hybrid algorithms -- Appendices. |